Summary In spite of many successes, genome-wide association studies (GWAS) face two major challenges. The ?rst is its limited statistical power even with thousands of individuals in a typical GWAS, thus missing many associated genetic variants, mostly single nucleotide polymorphisms (SNPs), due to their small effect sizes. The second is that even for those few identi?ed associated SNPs, since they often do not reside in protein-coding regions, it is dif?cult to interpret their function and thus missing biological insights about the disease (or other complex traits). This is evident with the limited success in genetic studies on Alzheimer's disease (AD) in spite of multiple genetic loci that have been identi?ed in the last few years. A new gene-based association test called PrediXcan was recently proposed to integrate GWAS with a reference eQTL dataset, alleviating the above two problems in boosting statistical power and facilitating biological interpretation of GWAS discoveries. Based on a novel reformulation of PrediXcan, we propose more powerful gene-based association tests, integrating GWAS with one or more sources of genomic and imaging endophenotypes. We also extend the proposed methods to the case with only GWAS summary statistics. The proposed methods are to be applied to several AD-related GWAS, eQTL and neuroimaging datasets for new discovery of AD-associated genetic variants. We will develop and distribute software implementing the proposed methods.